Technical Field
The present invention pertains generally to the field of diagnostics for rotating machinery, and in particular, the diagnosis of gear conditions.
Background
Gears are critical components in many important mechanical systems such as helicopters, power plants, and various industrial systems. The failure of a gear in one of these systems can lead to great financial losses as well as the loss of life. One way to mitigate the risk of such failures is to detect faults while there is still time to perform maintenance. As a result, many industries have adopted the use of health and usage monitoring systems (HUMS) and similar devices that are designed to collect data related to the condition of the system and provide the HUMS user with an alert when a fault is detected.
One of the most successful methods for detecting gear faults is through the collection of vibration data produced by the gears as they mesh with each other. Particularly for gears, the most effective diagnostics are based on synchronous averages (related to time-synchronous averages or synchronous-time averages). Synchronous averaging is a technique that uses information about the angular position of a shaft (usually from a tachometer) to resample and average the raw time-domain vibration data; this accentuates vibrations that are at a harmonic of the shaft speed (i.e., the synchronous signals) and minimizes other vibrations (i.e., the asynchronous noise). A variety of algorithms are applied to the synchronous average to produce condition indicators (CIs) for the gears on the shaft of interest. These CIs are generally scalar-valued data that correspond to the physical condition of the gears.
One of the great challenges to vibration-based gear diagnostics is that many CIs are sensitive to changes in operating conditions as well as changes in the physical condition of the gears. Their values are often dependent on torque, rotational speed, operating conditions that cannot easily be measured, and random noise.
A variety of techniques have been used to deal with the variability in gear CI values. One solution is limiting the operating regimes in which data is collected, but that has drawbacks. One such disadvantage is that meaningful data cannot be collected continuously during operations, reducing the amount and frequency of data collection and introducing unmonitored gaps. If the regimes are defined too broadly, there is too much variability; if the regimes are defined too narrowly, it is difficult to acquire enough data. Most importantly, the operating regime is not the only source of variability in the CI values, so there can still be high levels of noise.
Another set of approaches to dealing with noisy CI values is smoothing. This smoothing can use a simple moving average or median filter or a more complex model-based method such as a Kalman filter. In either case, smoothing comes with practical challenges and can make the system slower to respond to and alert the user to the presence of a fault. Effective use of this approach requires access to maintenance information so that the monitoring system can appropriately reset after any maintenance changes to the monitored system.
Due to the lack of a satisfactory way to compensate for CI sensitivity to operating condition and random variation, it can be difficult to separate the true indication of a fault from the noise. In fact many common CIs are ineffective in real-world applications because the noise level is too high, making the CI performance too poor to be useful in determining gear condition.